GFPose: Learning 3D Human Pose Prior with Gradient Fields

Understand

Learning 3D human pose prior is essential to human-centered AI.

• Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications.
• At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D human pose to match a given task specification.
• During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework.